Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations10
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 KiB
Average record size in memory160.0 B

Variable types

TimeSeries14
Categorical4
Numeric1

Timeseries statistics

Number of series14
Time series length10
Starting point1745135640000
Ending point1745136180000
Period60000
2025-05-20T09:53:20.242607image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:20.293156image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Alerts

close is highly overall correlated with high and 10 other fieldsHigh correlation
high is highly overall correlated with close and 12 other fieldsHigh correlation
low is highly overall correlated with close and 14 other fieldsHigh correlation
macd_7 is highly overall correlated with high and 13 other fieldsHigh correlation
macdhist_7 is highly overall correlated with close and 14 other fieldsHigh correlation
macdsignal_7 is highly overall correlated with close and 11 other fieldsHigh correlation
obv_7 is highly overall correlated with high and 3 other fieldsHigh correlation
open is highly overall correlated with close and 12 other fieldsHigh correlation
rsi_14 is highly overall correlated with close and 9 other fieldsHigh correlation
rsi_21 is highly overall correlated with close and 11 other fieldsHigh correlation
rsi_60 is highly overall correlated with close and 15 other fieldsHigh correlation
rsi_7 is highly overall correlated with rsi_60 and 1 other fieldsHigh correlation
sma_14 is highly overall correlated with low and 11 other fieldsHigh correlation
sma_21 is highly overall correlated with high and 12 other fieldsHigh correlation
sma_60 is highly overall correlated with close and 14 other fieldsHigh correlation
sma_7 is highly overall correlated with close and 13 other fieldsHigh correlation
target is highly overall correlated with high and 11 other fieldsHigh correlation
volume is highly overall correlated with high and 3 other fieldsHigh correlation
window_start_ms is highly overall correlated with close and 14 other fieldsHigh correlation
window_start_ms is non stationary Non stationary
low is non stationary Non stationary
volume is non stationary Non stationary
sma_7 is non stationary Non stationary
sma_60 is non stationary Non stationary
rsi_14 is non stationary Non stationary
macd_7 is non stationary Non stationary
macdsignal_7 is non stationary Non stationary
macdhist_7 is non stationary Non stationary
obv_7 is non stationary Non stationary
volume is seasonal Seasonal
obv_7 is seasonal Seasonal
window_start_ms is uniformly distributed Uniform
window_start_ms has unique values Unique
volume has unique values Unique
sma_7 has unique values Unique
sma_14 has unique values Unique
sma_21 has unique values Unique
sma_60 has unique values Unique
rsi_7 has unique values Unique
macd_7 has unique values Unique
macdsignal_7 has unique values Unique
macdhist_7 has unique values Unique
obv_7 has unique values Unique
rsi_7 has 1 (10.0%) zeros Zeros

Reproduction

Analysis started2025-05-20 07:53:15.443500
Analysis finished2025-05-20 07:53:20.222450
Duration4.78 seconds
Software versionydata-profiling vv4.16.1
Download configurationconfig.json

Variables

window_start_ms
Numeric time series

High correlation  Non stationary  Uniform  Unique 

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7451359 × 1012
Minimum1.7451356 × 1012
Maximum1.7451362 × 1012
Zeros0
Zeros (%)0.0%
Memory size160.0 B
2025-05-20T09:53:20.337810image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1.7451356 × 1012
5-th percentile1.7451357 × 1012
Q11.7451358 × 1012
median1.7451359 × 1012
Q31.745136 × 1012
95-th percentile1.7451362 × 1012
Maximum1.7451362 × 1012
Range540000
Interquartile range (IQR)270000

Descriptive statistics

Standard deviation181659.02
Coefficient of variation (CV)1.0409448 × 10-7
Kurtosis-1.2
Mean1.7451359 × 1012
Median Absolute Deviation (MAD)150000
Skewness0
Sum1.7451359 × 1013
Variance3.3 × 1010
MonotonicityStrictly increasing
Augmented Dickey-Fuller test p-value0.9585320601
2025-05-20T09:53:20.359431image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
2025-05-20T09:53:20.421036image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-20T09:53:20.436563image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
1.74513564 × 10121
10.0%
1.7451357 × 10121
10.0%
1.74513576 × 10121
10.0%
1.74513582 × 10121
10.0%
1.74513588 × 10121
10.0%
1.74513594 × 10121
10.0%
1.745136 × 10121
10.0%
1.74513606 × 10121
10.0%
1.74513612 × 10121
10.0%
1.74513618 × 10121
10.0%
ValueCountFrequency (%)
1.74513564 × 10121
10.0%
1.7451357 × 10121
10.0%
1.74513576 × 10121
10.0%
1.74513582 × 10121
10.0%
1.74513588 × 10121
10.0%
1.74513594 × 10121
10.0%
1.745136 × 10121
10.0%
1.74513606 × 10121
10.0%
1.74513612 × 10121
10.0%
1.74513618 × 10121
10.0%
ValueCountFrequency (%)
1.74513618 × 10121
10.0%
1.74513612 × 10121
10.0%
1.74513606 × 10121
10.0%
1.745136 × 10121
10.0%
1.74513594 × 10121
10.0%
1.74513588 × 10121
10.0%
1.74513582 × 10121
10.0%
1.74513576 × 10121
10.0%
1.7451357 × 10121
10.0%
1.74513564 × 10121
10.0%
2025-05-20T09:53:20.375816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

open
Categorical

High correlation 

Distinct4
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Memory size160.0 B
84788.1
84803.6
84752.8
84803.5

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters70
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)10.0%

Sample

1st row84803.6
2nd row84803.6
3rd row84803.6
4th row84803.5
5th row84788.1

Common Values

ValueCountFrequency (%)
84788.1 4
40.0%
84803.6 3
30.0%
84752.8 2
20.0%
84803.5 1
 
10.0%

Length

2025-05-20T09:53:20.462428image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-20T09:53:20.481476image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
84788.1 4
40.0%
84803.6 3
30.0%
84752.8 2
20.0%
84803.5 1
 
10.0%

Most occurring characters

ValueCountFrequency (%)
8 24
34.3%
4 10
14.3%
. 10
14.3%
7 6
 
8.6%
1 4
 
5.7%
0 4
 
5.7%
3 4
 
5.7%
6 3
 
4.3%
5 3
 
4.3%
2 2
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 70
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 24
34.3%
4 10
14.3%
. 10
14.3%
7 6
 
8.6%
1 4
 
5.7%
0 4
 
5.7%
3 4
 
5.7%
6 3
 
4.3%
5 3
 
4.3%
2 2
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 70
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 24
34.3%
4 10
14.3%
. 10
14.3%
7 6
 
8.6%
1 4
 
5.7%
0 4
 
5.7%
3 4
 
5.7%
6 3
 
4.3%
5 3
 
4.3%
2 2
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 70
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 24
34.3%
4 10
14.3%
. 10
14.3%
7 6
 
8.6%
1 4
 
5.7%
0 4
 
5.7%
3 4
 
5.7%
6 3
 
4.3%
5 3
 
4.3%
2 2
 
2.9%

high
Categorical

High correlation 

Distinct4
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Memory size160.0 B
84788.1
84803.6
84752.8
84803.5

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters70
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)10.0%

Sample

1st row84803.6
2nd row84803.6
3rd row84803.6
4th row84803.5
5th row84788.1

Common Values

ValueCountFrequency (%)
84788.1 4
40.0%
84803.6 3
30.0%
84752.8 2
20.0%
84803.5 1
 
10.0%

Length

2025-05-20T09:53:20.504187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-20T09:53:20.520028image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
84788.1 4
40.0%
84803.6 3
30.0%
84752.8 2
20.0%
84803.5 1
 
10.0%

Most occurring characters

ValueCountFrequency (%)
8 24
34.3%
4 10
14.3%
. 10
14.3%
7 6
 
8.6%
1 4
 
5.7%
0 4
 
5.7%
3 4
 
5.7%
6 3
 
4.3%
5 3
 
4.3%
2 2
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 70
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 24
34.3%
4 10
14.3%
. 10
14.3%
7 6
 
8.6%
1 4
 
5.7%
0 4
 
5.7%
3 4
 
5.7%
6 3
 
4.3%
5 3
 
4.3%
2 2
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 70
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 24
34.3%
4 10
14.3%
. 10
14.3%
7 6
 
8.6%
1 4
 
5.7%
0 4
 
5.7%
3 4
 
5.7%
6 3
 
4.3%
5 3
 
4.3%
2 2
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 70
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 24
34.3%
4 10
14.3%
. 10
14.3%
7 6
 
8.6%
1 4
 
5.7%
0 4
 
5.7%
3 4
 
5.7%
6 3
 
4.3%
5 3
 
4.3%
2 2
 
2.9%

low
Numeric time series

High correlation  Non stationary 

Distinct8
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84781.49
Minimum84751.5
Maximum84803.6
Zeros0
Zeros (%)0.0%
Memory size160.0 B
2025-05-20T09:53:20.542356image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum84751.5
5-th percentile84752.04
Q184760.4
median84788
Q384799.65
95-th percentile84803.555
Maximum84803.6
Range52.1
Interquartile range (IQR)39.25

Descriptive statistics

Standard deviation21.448877
Coefficient of variation (CV)0.00025299009
Kurtosis-1.3618881
Mean84781.49
Median Absolute Deviation (MAD)15.5
Skewness-0.58372624
Sum847814.9
Variance460.05433
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value1
2025-05-20T09:53:20.564840image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
2025-05-20T09:53:20.626079image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-20T09:53:20.640364image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
84803.5 2
20.0%
84788 2
20.0%
84803.6 1
10.0%
84783.2 1
10.0%
84788.1 1
10.0%
84751.5 1
10.0%
84752.7 1
10.0%
84752.8 1
10.0%
ValueCountFrequency (%)
84751.5 1
10.0%
84752.7 1
10.0%
84752.8 1
10.0%
84783.2 1
10.0%
84788 2
20.0%
84788.1 1
10.0%
84803.5 2
20.0%
84803.6 1
10.0%
ValueCountFrequency (%)
84803.6 1
10.0%
84803.5 2
20.0%
84788.1 1
10.0%
84788 2
20.0%
84783.2 1
10.0%
84752.8 1
10.0%
84752.7 1
10.0%
84751.5 1
10.0%
2025-05-20T09:53:20.581289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

close
Categorical

High correlation 

Distinct5
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size160.0 B
84788.1
84803.6
84751.5
84752.7
84752.8

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters70
Distinct characters10
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)30.0%

Sample

1st row84803.6
2nd row84803.6
3rd row84803.6
4th row84788.1
5th row84788.1

Common Values

ValueCountFrequency (%)
84788.1 4
40.0%
84803.6 3
30.0%
84751.5 1
 
10.0%
84752.7 1
 
10.0%
84752.8 1
 
10.0%

Length

2025-05-20T09:53:20.666324image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-20T09:53:20.685073image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
84788.1 4
40.0%
84803.6 3
30.0%
84751.5 1
 
10.0%
84752.7 1
 
10.0%
84752.8 1
 
10.0%

Most occurring characters

ValueCountFrequency (%)
8 22
31.4%
4 10
14.3%
. 10
14.3%
7 8
 
11.4%
1 5
 
7.1%
5 4
 
5.7%
0 3
 
4.3%
3 3
 
4.3%
6 3
 
4.3%
2 2
 
2.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 70
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 22
31.4%
4 10
14.3%
. 10
14.3%
7 8
 
11.4%
1 5
 
7.1%
5 4
 
5.7%
0 3
 
4.3%
3 3
 
4.3%
6 3
 
4.3%
2 2
 
2.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 70
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 22
31.4%
4 10
14.3%
. 10
14.3%
7 8
 
11.4%
1 5
 
7.1%
5 4
 
5.7%
0 3
 
4.3%
3 3
 
4.3%
6 3
 
4.3%
2 2
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 70
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 22
31.4%
4 10
14.3%
. 10
14.3%
7 8
 
11.4%
1 5
 
7.1%
5 4
 
5.7%
0 3
 
4.3%
3 3
 
4.3%
6 3
 
4.3%
2 2
 
2.9%

target
Categorical

High correlation 

Distinct4
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Memory size160.0 B
84752.8
84788.1
84751.5
84752.7

Length

Max length7
Median length7
Mean length7
Min length7

Characters and Unicode

Total characters70
Distinct characters7
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)20.0%

Sample

1st row84788.1
2nd row84788.1
3rd row84751.5
4th row84752.7
5th row84752.8

Common Values

ValueCountFrequency (%)
84752.8 6
60.0%
84788.1 2
 
20.0%
84751.5 1
 
10.0%
84752.7 1
 
10.0%

Length

2025-05-20T09:53:20.708000image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-05-20T09:53:20.725741image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
84752.8 6
60.0%
84788.1 2
 
20.0%
84751.5 1
 
10.0%
84752.7 1
 
10.0%

Most occurring characters

ValueCountFrequency (%)
8 20
28.6%
7 11
15.7%
4 10
14.3%
. 10
14.3%
5 9
12.9%
2 7
 
10.0%
1 3
 
4.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 70
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
8 20
28.6%
7 11
15.7%
4 10
14.3%
. 10
14.3%
5 9
12.9%
2 7
 
10.0%
1 3
 
4.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 70
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
8 20
28.6%
7 11
15.7%
4 10
14.3%
. 10
14.3%
5 9
12.9%
2 7
 
10.0%
1 3
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 70
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
8 20
28.6%
7 11
15.7%
4 10
14.3%
. 10
14.3%
5 9
12.9%
2 7
 
10.0%
1 3
 
4.3%

volume
Numeric time series

High correlation  Non stationary  Seasonal  Unique 

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.24152758
Minimum0.0004353
Maximum1.4261263
Zeros0
Zeros (%)0.0%
Memory size160.0 B
2025-05-20T09:53:20.748285image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0004353
5-th percentile0.0004485705
Q10.000820215
median0.009492745
Q30.099015033
95-th percentile1.1532832
Maximum1.4261263
Range1.425691
Interquartile range (IQR)0.098194818

Descriptive statistics

Standard deviation0.48748454
Coefficient of variation (CV)2.018339
Kurtosis3.7871716
Mean0.24152758
Median Absolute Deviation (MAD)0.0090427
Skewness2.1134498
Sum2.4152758
Variance0.23764118
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.9777089007
2025-05-20T09:53:20.773471image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
2025-05-20T09:53:20.838886image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-20T09:53:20.854334image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0127017 1
10.0%
0.00171246 1
10.0%
0.0005228 1
10.0%
1.4261263 1
10.0%
0.00046479 1
10.0%
0.12441984 1
10.0%
0.02280061 1
10.0%
0.8198082 1
10.0%
0.00628379 1
10.0%
0.0004353 1
10.0%
ValueCountFrequency (%)
0.0004353 1
10.0%
0.00046479 1
10.0%
0.0005228 1
10.0%
0.00171246 1
10.0%
0.00628379 1
10.0%
0.0127017 1
10.0%
0.02280061 1
10.0%
0.12441984 1
10.0%
0.8198082 1
10.0%
1.4261263 1
10.0%
ValueCountFrequency (%)
1.4261263 1
10.0%
0.8198082 1
10.0%
0.12441984 1
10.0%
0.02280061 1
10.0%
0.0127017 1
10.0%
0.00628379 1
10.0%
0.00171246 1
10.0%
0.0005228 1
10.0%
0.00046479 1
10.0%
0.0004353 1
10.0%
2025-05-20T09:53:20.790879image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

sma_7
Numeric time series

High correlation  Non stationary  Unique 

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84795.144
Minimum84772.771
Maximum84807.843
Zeros0
Zeros (%)0.0%
Memory size160.0 B
2025-05-20T09:53:20.877624image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum84772.771
5-th percentile84776.037
Q184789.161
median84798.05
Q384803.896
95-th percentile84807.11
Maximum84807.843
Range35.071429
Interquartile range (IQR)14.735714

Descriptive statistics

Standard deviation11.680875
Coefficient of variation (CV)0.00013775405
Kurtosis-0.14490725
Mean84795.144
Median Absolute Deviation (MAD)7.3428571
Skewness-0.91660096
Sum847951.44
Variance136.44283
MonotonicityStrictly decreasing
Augmented Dickey-Fuller test p-value0.9987498957
2025-05-20T09:53:20.902011image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
2025-05-20T09:53:20.965060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-20T09:53:20.980842image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
84807.84286 1
10.0%
84806.21429 1
10.0%
84804.57143 1
10.0%
84801.87143 1
10.0%
84799.15714 1
10.0%
84796.94286 1
10.0%
84794.74286 1
10.0%
84787.3 1
10.0%
84780.02857 1
10.0%
84772.77143 1
10.0%
ValueCountFrequency (%)
84772.77143 1
10.0%
84780.02857 1
10.0%
84787.3 1
10.0%
84794.74286 1
10.0%
84796.94286 1
10.0%
84799.15714 1
10.0%
84801.87143 1
10.0%
84804.57143 1
10.0%
84806.21429 1
10.0%
84807.84286 1
10.0%
ValueCountFrequency (%)
84807.84286 1
10.0%
84806.21429 1
10.0%
84804.57143 1
10.0%
84801.87143 1
10.0%
84799.15714 1
10.0%
84796.94286 1
10.0%
84794.74286 1
10.0%
84787.3 1
10.0%
84780.02857 1
10.0%
84772.77143 1
10.0%
2025-05-20T09:53:20.919108image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

sma_14
Numeric time series

High correlation  Unique 

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84785.419
Minimum84768.129
Maximum84797.571
Zeros0
Zeros (%)0.0%
Memory size160.0 B
2025-05-20T09:53:21.008149image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum84768.129
5-th percentile84770.06
Q184779.077
median84787.764
Q384792.666
95-th percentile84796.749
Maximum84797.571
Range29.442857
Interquartile range (IQR)13.589286

Descriptive statistics

Standard deviation9.9628226
Coefficient of variation (CV)0.00011750632
Kurtosis-0.81491087
Mean84785.419
Median Absolute Deviation (MAD)6.6678571
Skewness-0.59189597
Sum847854.19
Variance99.257835
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0
2025-05-20T09:53:21.030891image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
2025-05-20T09:53:21.095540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-20T09:53:21.111407image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
84768.12857 1
10.0%
84772.42143 1
10.0%
84777.96429 1
10.0%
84782.41429 1
10.0%
84786.85714 1
10.0%
84791.3 1
10.0%
84795.74286 1
10.0%
84797.57143 1
10.0%
84793.12143 1
10.0%
84788.67143 1
10.0%
ValueCountFrequency (%)
84768.12857 1
10.0%
84772.42143 1
10.0%
84777.96429 1
10.0%
84782.41429 1
10.0%
84786.85714 1
10.0%
84788.67143 1
10.0%
84791.3 1
10.0%
84793.12143 1
10.0%
84795.74286 1
10.0%
84797.57143 1
10.0%
ValueCountFrequency (%)
84797.57143 1
10.0%
84795.74286 1
10.0%
84793.12143 1
10.0%
84791.3 1
10.0%
84788.67143 1
10.0%
84786.85714 1
10.0%
84782.41429 1
10.0%
84777.96429 1
10.0%
84772.42143 1
10.0%
84768.12857 1
10.0%
2025-05-20T09:53:21.049242image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

sma_21
Numeric time series

High correlation  Unique 

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84770.333
Minimum84759.543
Maximum84776.233
Zeros0
Zeros (%)0.0%
Memory size160.0 B
2025-05-20T09:53:21.137086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum84759.543
5-th percentile84760.88
Q184766.893
median84772.414
Q384774.848
95-th percentile84775.721
Maximum84776.233
Range16.690476
Interquartile range (IQR)7.9547619

Descriptive statistics

Standard deviation5.8099139
Coefficient of variation (CV)6.8537113 × 10-5
Kurtosis-0.464533
Mean84770.333
Median Absolute Deviation (MAD)2.7119048
Skewness-0.92316521
Sum847703.33
Variance33.755099
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value2.464494675 × 10-7
2025-05-20T09:53:21.162953image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
2025-05-20T09:53:21.224153image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-20T09:53:21.238469image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
84759.54286 1
10.0%
84762.51429 1
10.0%
84765.96667 1
10.0%
84769.67143 1
10.0%
84771.5 1
10.0%
84773.32857 1
10.0%
84775.09524 1
10.0%
84774.51905 1
10.0%
84774.95714 1
10.0%
84776.23333 1
10.0%
ValueCountFrequency (%)
84759.54286 1
10.0%
84762.51429 1
10.0%
84765.96667 1
10.0%
84769.67143 1
10.0%
84771.5 1
10.0%
84773.32857 1
10.0%
84774.51905 1
10.0%
84774.95714 1
10.0%
84775.09524 1
10.0%
84776.23333 1
10.0%
ValueCountFrequency (%)
84776.23333 1
10.0%
84775.09524 1
10.0%
84774.95714 1
10.0%
84774.51905 1
10.0%
84773.32857 1
10.0%
84771.5 1
10.0%
84769.67143 1
10.0%
84765.96667 1
10.0%
84762.51429 1
10.0%
84759.54286 1
10.0%
2025-05-20T09:53:21.179587image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

sma_60
Numeric time series

High correlation  Non stationary  Unique 

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean84801.98
Minimum84789.56
Maximum84813.415
Zeros0
Zeros (%)0.0%
Memory size160.0 B
2025-05-20T09:53:21.263918image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum84789.56
5-th percentile84790.845
Q184795.993
median84802.227
Q384808.339
95-th percentile84812.426
Maximum84813.415
Range23.855
Interquartile range (IQR)12.34625

Descriptive statistics

Standard deviation8.1205031
Coefficient of variation (CV)9.5758414 × 10-5
Kurtosis-1.2478923
Mean84801.98
Median Absolute Deviation (MAD)6.8725
Skewness-0.11262092
Sum848019.8
Variance65.942571
MonotonicityStrictly decreasing
Augmented Dickey-Fuller test p-value1
2025-05-20T09:53:21.283404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
2025-05-20T09:53:21.340927image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-20T09:53:21.354095image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
84813.415 1
10.0%
84811.21667 1
10.0%
84809.01833 1
10.0%
84806.30167 1
10.0%
84803.585 1
10.0%
84800.86833 1
10.0%
84798.15167 1
10.0%
84795.27333 1
10.0%
84792.415 1
10.0%
84789.56 1
10.0%
ValueCountFrequency (%)
84789.56 1
10.0%
84792.415 1
10.0%
84795.27333 1
10.0%
84798.15167 1
10.0%
84800.86833 1
10.0%
84803.585 1
10.0%
84806.30167 1
10.0%
84809.01833 1
10.0%
84811.21667 1
10.0%
84813.415 1
10.0%
ValueCountFrequency (%)
84813.415 1
10.0%
84811.21667 1
10.0%
84809.01833 1
10.0%
84806.30167 1
10.0%
84803.585 1
10.0%
84800.86833 1
10.0%
84798.15167 1
10.0%
84795.27333 1
10.0%
84792.415 1
10.0%
84789.56 1
10.0%
2025-05-20T09:53:21.298148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

rsi_7
Real number (ℝ)

High correlation  Unique  Zeros 

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.012891
Minimum0
Maximum88.4273
Zeros1
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size160.0 B
2025-05-20T09:53:21.375728image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.234375
Q10.63796342
median1.3584708
Q32.3886945
95-th percentile49.760015
Maximum88.4273
Range88.4273
Interquartile range (IQR)1.7507311

Descriptive statistics

Standard deviation27.566161
Coefficient of variation (CV)2.7530672
Kurtosis9.9717879
Mean10.012891
Median Absolute Deviation (MAD)0.8652869
Skewness3.156201
Sum100.12891
Variance759.89326
MonotonicityNot monotonic
2025-05-20T09:53:21.392604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
88.4272997 1
10.0%
2.5 1
10.0%
1.680672269 1
10.0%
1.03626943 1
10.0%
0.5208333334 1
10.0%
0.6369426752 1
10.0%
0.6410256411 1
10.0%
0 1
10.0%
2.251407129 1
10.0%
2.434456929 1
10.0%
ValueCountFrequency (%)
0 1
10.0%
0.5208333334 1
10.0%
0.6369426752 1
10.0%
0.6410256411 1
10.0%
1.03626943 1
10.0%
1.680672269 1
10.0%
2.251407129 1
10.0%
2.434456929 1
10.0%
2.5 1
10.0%
88.4272997 1
10.0%
ValueCountFrequency (%)
88.4272997 1
10.0%
2.5 1
10.0%
2.434456929 1
10.0%
2.251407129 1
10.0%
1.680672269 1
10.0%
1.03626943 1
10.0%
0.6410256411 1
10.0%
0.6369426752 1
10.0%
0.5208333334 1
10.0%
0 1
10.0%

rsi_14
Numeric time series

High correlation  Non stationary 

Distinct7
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.758493
Minimum2.2970904
Maximum88.2643
Zeros0
Zeros (%)0.0%
Memory size160.0 B
2025-05-20T09:53:21.411130image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum2.2970904
5-th percentile2.2970904
Q159.863441
median75.972862
Q376.672384
95-th percentile83.056933
Maximum88.2643
Range85.967209
Interquartile range (IQR)16.808943

Descriptive statistics

Standard deviation31.31439
Coefficient of variation (CV)0.52401572
Kurtosis0.84727913
Mean59.758493
Median Absolute Deviation (MAD)6.1519422
Skewness-1.5065847
Sum597.58493
Variance980.59099
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.9628091077
2025-05-20T09:53:21.533335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
2025-05-20T09:53:21.589691image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-20T09:53:21.602114image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
76.67238422 3
30.0%
2.297090352 2
20.0%
64.38848921 1
 
10.0%
75.27333894 1
 
10.0%
88.2642998 1
 
10.0%
76.69237361 1
 
10.0%
58.35509138 1
 
10.0%
ValueCountFrequency (%)
2.297090352 2
20.0%
58.35509138 1
 
10.0%
64.38848921 1
 
10.0%
75.27333894 1
 
10.0%
76.67238422 3
30.0%
76.69237361 1
 
10.0%
88.2642998 1
 
10.0%
ValueCountFrequency (%)
88.2642998 1
 
10.0%
76.69237361 1
 
10.0%
76.67238422 3
30.0%
75.27333894 1
 
10.0%
64.38848921 1
 
10.0%
58.35509138 1
 
10.0%
2.297090352 2
20.0%
2025-05-20T09:53:21.546832image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

rsi_21
Numeric time series

High correlation 

Distinct9
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.306251
Minimum46.834118
Maximum68.719923
Zeros0
Zeros (%)0.0%
Memory size160.0 B
2025-05-20T09:53:21.626453image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum46.834118
5-th percentile49.460856
Q159.267532
median61.344489
Q363.334227
95-th percentile67.950815
Maximum68.719923
Range21.885805
Interquartile range (IQR)4.0666949

Descriptive statistics

Standard deviation6.4612746
Coefficient of variation (CV)0.10714104
Kurtosis1.1161083
Mean60.306251
Median Absolute Deviation (MAD)2.6610819
Skewness-1.0010178
Sum603.06251
Variance41.74807
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.001841773375
2025-05-20T09:53:21.645102image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
2025-05-20T09:53:21.701698image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-20T09:53:21.713744image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
61.40142518 2
20.0%
61.28755365 1
10.0%
63.97849462 1
10.0%
67.01079305 1
10.0%
68.719923 1
10.0%
61.10113704 1
10.0%
46.83411826 1
10.0%
52.67131243 1
10.0%
58.65633075 1
10.0%
ValueCountFrequency (%)
46.83411826 1
10.0%
52.67131243 1
10.0%
58.65633075 1
10.0%
61.10113704 1
10.0%
61.28755365 1
10.0%
61.40142518 2
20.0%
63.97849462 1
10.0%
67.01079305 1
10.0%
68.719923 1
10.0%
ValueCountFrequency (%)
68.719923 1
10.0%
67.01079305 1
10.0%
63.97849462 1
10.0%
61.40142518 2
20.0%
61.28755365 1
10.0%
61.10113704 1
10.0%
58.65633075 1
10.0%
52.67131243 1
10.0%
46.83411826 1
10.0%
2025-05-20T09:53:21.658191image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

rsi_60
Numeric time series

High correlation 

Distinct6
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.185059
Minimum34.864154
Maximum38.614212
Zeros0
Zeros (%)0.0%
Memory size160.0 B
2025-05-20T09:53:21.738411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum34.864154
5-th percentile34.925678
Q135.130572
median35.46719
Q337.548381
95-th percentile38.446767
Maximum38.614212
Range3.750058
Interquartile range (IQR)2.4178085

Descriptive statistics

Standard deviation1.5250978
Coefficient of variation (CV)0.042147169
Kurtosis-1.1700928
Mean36.185059
Median Absolute Deviation (MAD)0.4575693
Skewness0.9674712
Sum361.85059
Variance2.3259233
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0
2025-05-20T09:53:21.760763image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
2025-05-20T09:53:21.821414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-20T09:53:21.835426image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
35.46718973 4
40.0%
38.24211089 2
20.0%
38.6142123 1
 
10.0%
34.86415425 1
 
10.0%
35.00087458 1
 
10.0%
35.01836628 1
 
10.0%
ValueCountFrequency (%)
34.86415425 1
 
10.0%
35.00087458 1
 
10.0%
35.01836628 1
 
10.0%
35.46718973 4
40.0%
38.24211089 2
20.0%
38.6142123 1
 
10.0%
ValueCountFrequency (%)
38.6142123 1
 
10.0%
38.24211089 2
20.0%
35.46718973 4
40.0%
35.01836628 1
 
10.0%
35.00087458 1
 
10.0%
34.86415425 1
 
10.0%
2025-05-20T09:53:21.777118image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

macd_7
Numeric time series

High correlation  Non stationary  Unique 

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1579441
Minimum-6.2549175
Maximum14.026928
Zeros0
Zeros (%)0.0%
Memory size160.0 B
2025-05-20T09:53:21.859116image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-6.2549175
5-th percentile-5.4065115
Q10.10597017
median5.9105032
Q311.030866
95-th percentile13.545981
Maximum14.026928
Range20.281845
Interquartile range (IQR)10.924896

Descriptive statistics

Standard deviation7.1630996
Coefficient of variation (CV)1.3887509
Kurtosis-1.1234953
Mean5.1579441
Median Absolute Deviation (MAD)6.4436525
Skewness-0.4132058
Sum51.579441
Variance51.309996
MonotonicityStrictly decreasing
Augmented Dickey-Fuller test p-value1
2025-05-20T09:53:21.882004image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
2025-05-20T09:53:21.943900image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-20T09:53:21.958651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
14.02692792 1
10.0%
12.95815744 1
10.0%
11.7501539 1
10.0%
8.87300212 1
10.0%
6.716434783 1
10.0%
5.10457156 1
10.0%
3.950082563 1
10.0%
-1.175400624 1
10.0%
-4.36957083 1
10.0%
-6.254917486 1
10.0%
ValueCountFrequency (%)
-6.254917486 1
10.0%
-4.36957083 1
10.0%
-1.175400624 1
10.0%
3.950082563 1
10.0%
5.10457156 1
10.0%
6.716434783 1
10.0%
8.87300212 1
10.0%
11.7501539 1
10.0%
12.95815744 1
10.0%
14.02692792 1
10.0%
ValueCountFrequency (%)
14.02692792 1
10.0%
12.95815744 1
10.0%
11.7501539 1
10.0%
8.87300212 1
10.0%
6.716434783 1
10.0%
5.10457156 1
10.0%
3.950082563 1
10.0%
-1.175400624 1
10.0%
-4.36957083 1
10.0%
-6.254917486 1
10.0%
2025-05-20T09:53:21.898541image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

macdsignal_7
Numeric time series

High correlation  Non stationary  Unique 

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.91317834
Minimum-1.3316105
Maximum4.8762124
Zeros0
Zeros (%)0.0%
Memory size160.0 B
2025-05-20T09:53:21.983335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-1.3316105
5-th percentile-1.1467943
Q1-0.478574
median0.22830004
Q32.0363378
95-th percentile4.2074472
Maximum4.8762124
Range6.2078229
Interquartile range (IQR)2.5149118

Descriptive statistics

Standard deviation2.0233989
Coefficient of variation (CV)2.2157763
Kurtosis-0.014596002
Mean0.91317834
Median Absolute Deviation (MAD)1.0430993
Skewness0.95984467
Sum9.1317834
Variance4.0941431
MonotonicityStrictly increasing
Augmented Dickey-Fuller test p-value1
2025-05-20T09:53:22.003522image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
2025-05-20T09:53:22.063491image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-20T09:53:22.077328image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
-1.331610498 1
10.0%
-0.9209077991 1
10.0%
-0.541870144 1
10.0%
-0.2886855744 1
10.0%
0.08270719876 1
10.0%
0.3738928744 1
10.0%
1.165290737 1
10.0%
2.326686781 1
10.0%
3.390067437 1
10.0%
4.8762124 1
10.0%
ValueCountFrequency (%)
-1.331610498 1
10.0%
-0.9209077991 1
10.0%
-0.541870144 1
10.0%
-0.2886855744 1
10.0%
0.08270719876 1
10.0%
0.3738928744 1
10.0%
1.165290737 1
10.0%
2.326686781 1
10.0%
3.390067437 1
10.0%
4.8762124 1
10.0%
ValueCountFrequency (%)
4.8762124 1
10.0%
3.390067437 1
10.0%
2.326686781 1
10.0%
1.165290737 1
10.0%
0.3738928744 1
10.0%
0.08270719876 1
10.0%
-0.2886855744 1
10.0%
-0.541870144 1
10.0%
-0.9209077991 1
10.0%
-1.331610498 1
10.0%
2025-05-20T09:53:22.018497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

macdhist_7
Numeric time series

High correlation  Non stationary  Unique 

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2447658
Minimum-11.13113
Maximum15.358538
Zeros0
Zeros (%)0.0%
Memory size160.0 B
2025-05-20T09:53:22.099873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum-11.13113
5-th percentile-9.6139587
Q1-1.9303676
median5.6822031
Q311.50944
95-th percentile14.692775
Maximum15.358538
Range26.489668
Interquartile range (IQR)13.439808

Descriptive statistics

Standard deviation9.1523072
Coefficient of variation (CV)2.1561395
Kurtosis-0.95036327
Mean4.2447658
Median Absolute Deviation (MAD)7.4033415
Skewness-0.52761664
Sum42.447658
Variance83.764727
MonotonicityStrictly decreasing
Augmented Dickey-Fuller test p-value1
2025-05-20T09:53:22.120677image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
2025-05-20T09:53:22.177681image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-20T09:53:22.190613image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
15.35853842 1
10.0%
13.87906524 1
10.0%
12.29202404 1
10.0%
9.161687694 1
10.0%
6.633727585 1
10.0%
4.730678686 1
10.0%
2.784791826 1
10.0%
-3.502087405 1
10.0%
-7.759638268 1
10.0%
-11.13112989 1
10.0%
ValueCountFrequency (%)
-11.13112989 1
10.0%
-7.759638268 1
10.0%
-3.502087405 1
10.0%
2.784791826 1
10.0%
4.730678686 1
10.0%
6.633727585 1
10.0%
9.161687694 1
10.0%
12.29202404 1
10.0%
13.87906524 1
10.0%
15.35853842 1
10.0%
ValueCountFrequency (%)
15.35853842 1
10.0%
13.87906524 1
10.0%
12.29202404 1
10.0%
9.161687694 1
10.0%
6.633727585 1
10.0%
4.730678686 1
10.0%
2.784791826 1
10.0%
-3.502087405 1
10.0%
-7.759638268 1
10.0%
-11.13112989 1
10.0%
2025-05-20T09:53:22.134873image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

obv_7
Numeric time series

High correlation  Non stationary  Seasonal  Unique 

Distinct10
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.24152758
Minimum0.0004353
Maximum1.4261263
Zeros0
Zeros (%)0.0%
Memory size160.0 B
2025-05-20T09:53:22.216779image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.0004353
5-th percentile0.0004485705
Q10.000820215
median0.009492745
Q30.099015033
95-th percentile1.1532832
Maximum1.4261263
Range1.425691
Interquartile range (IQR)0.098194818

Descriptive statistics

Standard deviation0.48748454
Coefficient of variation (CV)2.018339
Kurtosis3.7871716
Mean0.24152758
Median Absolute Deviation (MAD)0.0090427
Skewness2.1134498
Sum2.4152758
Variance0.23764118
MonotonicityNot monotonic
Augmented Dickey-Fuller test p-value0.9777089007
2025-05-20T09:53:22.239682image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
2025-05-20T09:53:22.299830image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Gap statistics

number of gaps0
min0
max0
mean0
std0
2025-05-20T09:53:22.313645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
0.0127017 1
10.0%
0.00171246 1
10.0%
0.0005228 1
10.0%
1.4261263 1
10.0%
0.00046479 1
10.0%
0.12441984 1
10.0%
0.02280061 1
10.0%
0.8198082 1
10.0%
0.00628379 1
10.0%
0.0004353 1
10.0%
ValueCountFrequency (%)
0.0004353 1
10.0%
0.00046479 1
10.0%
0.0005228 1
10.0%
0.00171246 1
10.0%
0.00628379 1
10.0%
0.0127017 1
10.0%
0.02280061 1
10.0%
0.12441984 1
10.0%
0.8198082 1
10.0%
1.4261263 1
10.0%
ValueCountFrequency (%)
1.4261263 1
10.0%
0.8198082 1
10.0%
0.12441984 1
10.0%
0.02280061 1
10.0%
0.0127017 1
10.0%
0.00628379 1
10.0%
0.00171246 1
10.0%
0.0005228 1
10.0%
0.00046479 1
10.0%
0.0004353 1
10.0%
2025-05-20T09:53:22.254764image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ACF and PACF

Interactions

2025-05-20T09:53:19.754604image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:15.625200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:15.995339image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.254486image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.532735image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.920975image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.203647image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.505409image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.761060image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.009006image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.257663image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.598270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.878553image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.208286image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.480887image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.774143image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:15.661664image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.014359image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.273418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.552671image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.940552image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.225132image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.523524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.778557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.027527image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.274573image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.617234image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.899722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.226805image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.499875image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.793500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:15.685271image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.030819image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.291785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.569488image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.960263image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.245123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.540592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.794589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.043836image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.289769image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.635432image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.918230image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.244027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.517684image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.811978image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:15.722511image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.048612image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.309038image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.588986image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.979733image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.265500image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.558335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.812330image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.060925image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.410394image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.653999image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.938318image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.263493image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.536946image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.832341image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:15.744932image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.065966image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.328748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.606882image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.999874image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.286770image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.576307image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.829205image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.078645image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.426289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.673620image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.957122image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.281475image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.554501image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.850778image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:15.776023image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.083209image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.346165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.729121image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.017756image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.306641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.592312image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.846376image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.094052image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.442826image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.691907image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.976599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.299418image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.573001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.871196image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:15.806719image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.100746image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.367350image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.747753image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.037524image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.327377image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.610168image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.864093image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.111993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.458980image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.712172image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.996497image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.317266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.592595image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.888899image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:15.831699image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.116208image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.385470image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.766042image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.055148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.346335image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.626040image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.880270image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.128018image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.474178image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.730008image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.015608image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.334623image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.609913image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.907027image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:15.856293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.132668image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.403059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.783759image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.072069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.366041image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.641856image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.894442image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.144131image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.488424image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.747450image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.066358image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.350776image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.628266image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.923871image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:15.875659image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.148739image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.420419image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.802067image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.090441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.385583image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.657581image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.908976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.158796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.502910image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.765058image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.091651image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.368820image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.645129image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.941569image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:15.893839image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.165348image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.437714image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.820221image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.107222image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.404189image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.672534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.923959image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.173112image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.516378image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.783758image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.109197image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.386421image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.661404image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.960974image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:15.914748image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.182740image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.456083image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.840686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.127059image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.425575image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.691408image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.941785image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.190717image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.533289image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.801780image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.130510image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.406637image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.679931image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:20.087796image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:15.935564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.202277image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.477434image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.860720image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.146940image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.445246image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.709846image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.959660image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.208401image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.550625image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.822076image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.149991image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.425955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.700518image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:20.106789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:15.956096image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.219399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.495817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.880487image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.165389image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.466630image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.727917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.975397image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.225472image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.566387image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.840262image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.169483image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.444357image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.717589image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:20.125917image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:15.976020image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.236024image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.514872image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:16.900259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.183014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.484861image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.743897image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:17.992374image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.241001image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.581241image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:18.859306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.188386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.462494image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-05-20T09:53:19.736243image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-05-20T09:53:22.335429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
closehighlowmacd_7macdhist_7macdsignal_7obv_7openrsi_14rsi_21rsi_60rsi_7sma_14sma_21sma_60sma_7targetvolumewindow_start_ms
close1.0000.6040.5770.4080.7560.7220.4150.6040.5830.6870.8450.0000.0000.0000.7070.8160.0000.4151.000
high0.6041.0000.8660.7070.6550.4080.5611.0000.3870.3390.7590.0000.4330.5000.6120.7070.7070.5611.000
low0.5770.8661.0000.8350.835-0.835-0.3540.8660.5760.6760.9440.372-0.781-0.7380.8350.8350.707-0.354-0.835
macd_70.4080.7070.8351.0001.000-1.0000.0670.7070.5420.7170.9160.358-0.842-0.9641.0001.0000.7070.067-1.000
macdhist_70.7560.6550.8351.0001.000-1.0000.0670.6550.5420.7170.9160.358-0.842-0.9641.0001.0000.6550.067-1.000
macdsignal_70.7220.408-0.835-1.000-1.0001.000-0.0670.408-0.542-0.717-0.916-0.3580.8420.964-1.000-1.0000.000-0.0671.000
obv_70.4150.561-0.3540.0670.067-0.0671.0000.5610.2030.018-0.157-0.3700.309-0.0670.0670.0670.5041.000-0.067
open0.6041.0000.8660.7070.6550.4080.5611.0000.3870.3390.7590.0000.4330.5000.6120.7070.7070.5611.000
rsi_140.5830.3870.5760.5420.542-0.5420.2030.3871.0000.8270.516-0.295-0.302-0.4250.5420.5420.3650.203-0.542
rsi_210.6870.3390.6760.7170.717-0.7170.0180.3390.8271.0000.6860.134-0.687-0.6570.7170.7170.0000.018-0.717
rsi_600.8450.7590.9440.9160.916-0.916-0.1570.7590.5160.6861.0000.514-0.859-0.8160.9160.9160.543-0.157-0.916
rsi_70.0000.0000.3720.3580.358-0.358-0.3700.000-0.2950.1340.5141.000-0.673-0.3580.3580.3580.354-0.370-0.358
sma_140.0000.433-0.781-0.842-0.8420.8420.3090.433-0.302-0.687-0.859-0.6731.0000.842-0.842-0.8420.6550.3090.842
sma_210.0000.500-0.738-0.964-0.9640.964-0.0670.500-0.425-0.657-0.816-0.3580.8421.000-0.964-0.9640.707-0.0670.964
sma_600.7070.6120.8351.0001.000-1.0000.0670.6120.5420.7170.9160.358-0.842-0.9641.0001.0000.6120.067-1.000
sma_70.8160.7070.8351.0001.000-1.0000.0670.7070.5420.7170.9160.358-0.842-0.9641.0001.0000.0000.067-1.000
target0.0000.7070.7070.7070.6550.0000.5040.7070.3650.0000.5430.3540.6550.7070.6120.0001.0000.5041.000
volume0.4150.561-0.3540.0670.067-0.0671.0000.5610.2030.018-0.157-0.3700.309-0.0670.0670.0670.5041.000-0.067
window_start_ms1.0001.000-0.835-1.000-1.0001.000-0.0671.000-0.542-0.717-0.916-0.3580.8420.964-1.000-1.0001.000-0.0671.000

Missing values

2025-05-20T09:53:20.160802image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-05-20T09:53:20.194533image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

window_start_msopenhighlowclosetargetvolumesma_7sma_14sma_21sma_60rsi_7rsi_14rsi_21rsi_60macd_7macdsignal_7macdhist_7obv_7
1745135640000174513564000084803.6084803.6084803.5084803.6084788.100.0184807.8484768.1384759.5484813.4188.4364.3961.2938.6114.03-1.3315.360.01
1745135700000174513570000084803.6084803.6084803.5084803.6084788.100.0084806.2184772.4284762.5184811.222.5075.2763.9838.2412.96-0.9213.880.00
1745135760000174513576000084803.6084803.6084803.6084803.6084751.500.0084804.5784777.9684765.9784809.021.6888.2667.0138.2411.75-0.5412.290.00
1745135820000174513582000084803.5084803.5084783.2084788.1084752.701.4384801.8784782.4184769.6784806.301.0476.6968.7235.478.87-0.299.161.43
1745135880000174513588000084788.1084788.1084788.1084788.1084752.800.0084799.1684786.8684771.5084803.580.5276.6761.4035.476.720.086.630.00
1745135940000174513594000084788.1084788.1084788.0084788.1084752.800.1284796.9484791.3084773.3384800.870.6476.6761.4035.475.100.374.730.12
1745136000000174513600000084788.1084788.1084788.0084788.1084752.800.0284794.7484795.7484775.1084798.150.6476.6761.1035.473.951.172.780.02
1745136060000174513606000084788.1084788.1084751.5084751.5084752.800.8284787.3084797.5784774.5284795.270.0058.3646.8334.86-1.182.33-3.500.82
1745136120000174513612000084752.8084752.8084752.7084752.7084752.800.0184780.0384793.1284774.9684792.412.252.3052.6735.00-4.373.39-7.760.01
1745136180000174513618000084752.8084752.8084752.8084752.8084752.800.0084772.7784788.6784776.2384789.562.432.3058.6635.02-6.254.88-11.130.00
window_start_msopenhighlowclosetargetvolumesma_7sma_14sma_21sma_60rsi_7rsi_14rsi_21rsi_60macd_7macdsignal_7macdhist_7obv_7
1745135640000174513564000084803.6084803.6084803.5084803.6084788.100.0184807.8484768.1384759.5484813.4188.4364.3961.2938.6114.03-1.3315.360.01
1745135700000174513570000084803.6084803.6084803.5084803.6084788.100.0084806.2184772.4284762.5184811.222.5075.2763.9838.2412.96-0.9213.880.00
1745135760000174513576000084803.6084803.6084803.6084803.6084751.500.0084804.5784777.9684765.9784809.021.6888.2667.0138.2411.75-0.5412.290.00
1745135820000174513582000084803.5084803.5084783.2084788.1084752.701.4384801.8784782.4184769.6784806.301.0476.6968.7235.478.87-0.299.161.43
1745135880000174513588000084788.1084788.1084788.1084788.1084752.800.0084799.1684786.8684771.5084803.580.5276.6761.4035.476.720.086.630.00
1745135940000174513594000084788.1084788.1084788.0084788.1084752.800.1284796.9484791.3084773.3384800.870.6476.6761.4035.475.100.374.730.12
1745136000000174513600000084788.1084788.1084788.0084788.1084752.800.0284794.7484795.7484775.1084798.150.6476.6761.1035.473.951.172.780.02
1745136060000174513606000084788.1084788.1084751.5084751.5084752.800.8284787.3084797.5784774.5284795.270.0058.3646.8334.86-1.182.33-3.500.82
1745136120000174513612000084752.8084752.8084752.7084752.7084752.800.0184780.0384793.1284774.9684792.412.252.3052.6735.00-4.373.39-7.760.01
1745136180000174513618000084752.8084752.8084752.8084752.8084752.800.0084772.7784788.6784776.2384789.562.432.3058.6635.02-6.254.88-11.130.00